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  <h1>Source code for torch.distributions.distribution</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">constraints</span>
<span class="kn">from</span> <span class="nn">torch.distributions.utils</span> <span class="kn">import</span> <span class="n">lazy_property</span>


<div class="viewcode-block" id="Distribution"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution">[docs]</a><span class="k">class</span> <span class="nc">Distribution</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Distribution is the abstract base class for probability distributions.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="n">has_rsample</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="n">has_enumerate_support</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="n">_validate_args</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="n">support</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="nd">@staticmethod</span>
    <span class="k">def</span> <span class="nf">set_default_validate_args</span><span class="p">(</span><span class="n">value</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">value</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span>
        <span class="n">Distribution</span><span class="o">.</span><span class="n">_validate_args</span> <span class="o">=</span> <span class="n">value</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">event_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(),</span> <span class="n">validate_args</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span> <span class="o">=</span> <span class="n">batch_shape</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span> <span class="o">=</span> <span class="n">event_shape</span>
        <span class="k">if</span> <span class="n">validate_args</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span> <span class="o">=</span> <span class="n">validate_args</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span><span class="p">:</span>
            <span class="k">for</span> <span class="n">param</span><span class="p">,</span> <span class="n">constraint</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_constraints</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="k">if</span> <span class="n">constraints</span><span class="o">.</span><span class="n">is_dependent</span><span class="p">(</span><span class="n">constraint</span><span class="p">):</span>
                    <span class="k">continue</span>  <span class="c1"># skip constraints that cannot be checked</span>
                <span class="k">if</span> <span class="n">param</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">param</span><span class="p">),</span> <span class="n">lazy_property</span><span class="p">):</span>
                    <span class="k">continue</span>  <span class="c1"># skip checking lazily-constructed args</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="n">constraint</span><span class="o">.</span><span class="n">check</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">param</span><span class="p">))</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The parameter </span><span class="si">{}</span><span class="s2"> has invalid values&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">param</span><span class="p">))</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Distribution</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

<div class="viewcode-block" id="Distribution.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.expand">[docs]</a>    <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_shape</span><span class="p">,</span> <span class="n">_instance</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns a new distribution instance (or populates an existing instance</span>
<span class="sd">        provided by a derived class) with batch dimensions expanded to</span>
<span class="sd">        `batch_shape`. This method calls :class:`~torch.Tensor.expand` on</span>
<span class="sd">        the distribution&#39;s parameters. As such, this does not allocate new</span>
<span class="sd">        memory for the expanded distribution instance. Additionally,</span>
<span class="sd">        this does not repeat any args checking or parameter broadcasting in</span>
<span class="sd">        `__init__.py`, when an instance is first created.</span>

<span class="sd">        Args:</span>
<span class="sd">            batch_shape (torch.Size): the desired expanded size.</span>
<span class="sd">            _instance: new instance provided by subclasses that</span>
<span class="sd">                need to override `.expand`.</span>

<span class="sd">        Returns:</span>
<span class="sd">            New distribution instance with batch dimensions expanded to</span>
<span class="sd">            `batch_size`.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">batch_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the shape over which parameters are batched.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">event_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the shape of a single sample (without batching).</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">arg_constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns a dictionary from argument names to</span>
<span class="sd">        :class:`~torch.distributions.constraints.Constraint` objects that</span>
<span class="sd">        should be satisfied by each argument of this distribution. Args that</span>
<span class="sd">        are not tensors need not appear in this dict.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">support</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns a :class:`~torch.distributions.constraints.Constraint` object</span>
<span class="sd">        representing this distribution&#39;s support.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the mean of the distribution.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">variance</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the variance of the distribution.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">stddev</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the standard deviation of the distribution.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">variance</span><span class="o">.</span><span class="n">sqrt</span><span class="p">()</span>

<div class="viewcode-block" id="Distribution.sample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.sample">[docs]</a>    <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">()):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Generates a sample_shape shaped sample or sample_shape shaped batch of</span>
<span class="sd">        samples if the distribution parameters are batched.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">rsample</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span></div>

<div class="viewcode-block" id="Distribution.rsample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.rsample">[docs]</a>    <span class="k">def</span> <span class="nf">rsample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">()):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Generates a sample_shape shaped reparameterized sample or sample_shape</span>
<span class="sd">        shaped batch of reparameterized samples if the distribution parameters</span>
<span class="sd">        are batched.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

<div class="viewcode-block" id="Distribution.sample_n"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.sample_n">[docs]</a>    <span class="k">def</span> <span class="nf">sample_n</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Generates n samples or n batches of samples if the distribution</span>
<span class="sd">        parameters are batched.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;sample_n will be deprecated. Use .sample((n,)) instead&#39;</span><span class="p">,</span> <span class="ne">UserWarning</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">((</span><span class="n">n</span><span class="p">,)))</span></div>

<div class="viewcode-block" id="Distribution.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.log_prob">[docs]</a>    <span class="k">def</span> <span class="nf">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the log of the probability density/mass function evaluated at</span>
<span class="sd">        `value`.</span>

<span class="sd">        Args:</span>
<span class="sd">            value (Tensor):</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

<div class="viewcode-block" id="Distribution.cdf"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.cdf">[docs]</a>    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the cumulative density/mass function evaluated at</span>
<span class="sd">        `value`.</span>

<span class="sd">        Args:</span>
<span class="sd">            value (Tensor):</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

<div class="viewcode-block" id="Distribution.icdf"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.icdf">[docs]</a>    <span class="k">def</span> <span class="nf">icdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the inverse cumulative density/mass function evaluated at</span>
<span class="sd">        `value`.</span>

<span class="sd">        Args:</span>
<span class="sd">            value (Tensor):</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

<div class="viewcode-block" id="Distribution.enumerate_support"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.enumerate_support">[docs]</a>    <span class="k">def</span> <span class="nf">enumerate_support</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">expand</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns tensor containing all values supported by a discrete</span>
<span class="sd">        distribution. The result will enumerate over dimension 0, so the shape</span>
<span class="sd">        of the result will be `(cardinality,) + batch_shape + event_shape`</span>
<span class="sd">        (where `event_shape = ()` for univariate distributions).</span>

<span class="sd">        Note that this enumerates over all batched tensors in lock-step</span>
<span class="sd">        `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens</span>
<span class="sd">        along dim 0, but with the remaining batch dimensions being</span>
<span class="sd">        singleton dimensions, `[[0], [1], ..`.</span>

<span class="sd">        To iterate over the full Cartesian product use</span>
<span class="sd">        `itertools.product(m.enumerate_support())`.</span>

<span class="sd">        Args:</span>
<span class="sd">            expand (bool): whether to expand the support over the</span>
<span class="sd">                batch dims to match the distribution&#39;s `batch_shape`.</span>

<span class="sd">        Returns:</span>
<span class="sd">            Tensor iterating over dimension 0.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

<div class="viewcode-block" id="Distribution.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.entropy">[docs]</a>    <span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns entropy of distribution, batched over batch_shape.</span>

<span class="sd">        Returns:</span>
<span class="sd">            Tensor of shape batch_shape.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>

<div class="viewcode-block" id="Distribution.perplexity"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.distribution.Distribution.perplexity">[docs]</a>    <span class="k">def</span> <span class="nf">perplexity</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns perplexity of distribution, batched over batch_shape.</span>

<span class="sd">        Returns:</span>
<span class="sd">            Tensor of shape batch_shape.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">entropy</span><span class="p">())</span></div>

    <span class="k">def</span> <span class="nf">_extended_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">()):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Returns the size of the sample returned by the distribution, given</span>
<span class="sd">        a `sample_shape`. Note, that the batch and event shapes of a distribution</span>
<span class="sd">        instance are fixed at the time of construction. If this is empty, the</span>
<span class="sd">        returned shape is upcast to (1,).</span>

<span class="sd">        Args:</span>
<span class="sd">            sample_shape (torch.Size): the size of the sample to be drawn.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
            <span class="n">sample_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">sample_shape</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span>

    <span class="k">def</span> <span class="nf">_validate_sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Argument validation for distribution methods such as `log_prob`,</span>
<span class="sd">        `cdf` and `icdf`. The rightmost dimensions of a value to be</span>
<span class="sd">        scored via these methods must agree with the distribution&#39;s batch</span>
<span class="sd">        and event shapes.</span>

<span class="sd">        Args:</span>
<span class="sd">            value (Tensor): the tensor whose log probability is to be</span>
<span class="sd">                computed by the `log_prob` method.</span>
<span class="sd">        Raises</span>
<span class="sd">            ValueError: when the rightmost dimensions of `value` do not match the</span>
<span class="sd">                distribution&#39;s batch and event shapes.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The value argument to log_prob must be a Tensor&#39;</span><span class="p">)</span>

        <span class="n">event_dim_start</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">size</span><span class="p">())</span> <span class="o">-</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">value</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="n">event_dim_start</span><span class="p">:]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The right-most size of value must match event_shape: </span><span class="si">{}</span><span class="s1"> vs </span><span class="si">{}</span><span class="s1">.&#39;</span><span class="o">.</span>
                             <span class="nb">format</span><span class="p">(</span><span class="n">value</span><span class="o">.</span><span class="n">size</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span><span class="p">))</span>

        <span class="n">actual_shape</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
        <span class="n">expected_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_event_shape</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="n">actual_shape</span><span class="p">),</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">expected_shape</span><span class="p">)):</span>
            <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">!=</span> <span class="n">j</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Value is not broadcastable with batch_shape+event_shape: </span><span class="si">{}</span><span class="s1"> vs </span><span class="si">{}</span><span class="s1">.&#39;</span><span class="o">.</span>
                                 <span class="nb">format</span><span class="p">(</span><span class="n">actual_shape</span><span class="p">,</span> <span class="n">expected_shape</span><span class="p">))</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">support</span><span class="o">.</span><span class="n">check</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The value argument must be within the support&#39;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_get_checked_instance</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">cls</span><span class="p">,</span> <span class="n">_instance</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">_instance</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span> <span class="o">!=</span> <span class="bp">cls</span><span class="o">.</span><span class="fm">__init__</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">&quot;Subclass </span><span class="si">{}</span><span class="s2"> of </span><span class="si">{}</span><span class="s2"> that defines a custom __init__ method &quot;</span>
                                      <span class="s2">&quot;must also define a custom .expand() method.&quot;</span><span class="o">.</span>
                                      <span class="nb">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span> <span class="k">if</span> <span class="n">_instance</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">_instance</span>

    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">param_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_constraints</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">]</span>
        <span class="n">args_string</span> <span class="o">=</span> <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1">: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">p</span><span class="p">]</span>
                                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">p</span><span class="p">]</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span>
                                <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">p</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">())</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">param_names</span><span class="p">])</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">+</span> <span class="s1">&#39;(&#39;</span> <span class="o">+</span> <span class="n">args_string</span> <span class="o">+</span> <span class="s1">&#39;)&#39;</span></div>
</pre></div>

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